Drug discovery in rheumatoid arthritis with joint effusion identified by text mining and biomedical databases

Abstract

Background: Rheumatoid arthritis is a long-term systemic disease that primarily affects multiple synovial joints throughout the body. Some patients with severe joint effusion even require repeated arthrocentesis or arthroscopic debridement to relieve symptoms, which causes them much suffering mentally and physically. This text-mining study was designed to find potential drugs that target key genes in this disease. Methods: Firstly, we performed text mining by two keywords (“rheumatoid synovitis” and “joint effusion”) to get a common set of genes. Secondly, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis performed on these genes, and protein-protein interaction (PPI) network was constructed. Subsequently, the significant genes clustered in the PPI network were chose to execute gene-drug interaction analysis for potential drug discovery. Results: Through text mining, 68 overlapping genes were identified as an initial set of key genes. Construction of the initial gene set’s PPI network showed that 25 genes clustered in a significant gene module. Ultimately, 8 out of 25 genes could be targetable by a total of 19 drugs. Conclusions: The final 8 genes (PTGS2, TNF, VEGFA, IL1B, CCL2, VWF, IL6, and ESR1) and 19 drugs may provide significant therapeutic value for rheumatoid arthritis patients with joint effusion.